The present invention relates to a method of detecting differences in breast tissue in subsequent images of the same breast.
Breast cancer is one of the largest serious diseases among women in the western world. It is the most common cancer in women accounting for nearly one out of every three cancers diagnosed in the United States. It is also the most common and deadly cancer for women on a global scale, where breast cancer accounts for 21% of all cancer cases and 14% of all cancer deaths.
However, if detected sufficiently early, there is a high probability of survival. Detection of breast cancer can be very difficult, since the first signs of breast cancer are often asymptomatic.
Mammograms have thus far been found to be the most effective way to detect breast cancer early, sometimes up to two years before a lump in the breast can be felt. Mammography is a specific type of imaging that uses a low-dose x-ray system. Once an image has been developed, doctors examine the image to look for signs that cancer is developing. Naturally, where human intervention is required, there is room for error or misjudgment. A lot of effort has therefore been put into the field of improving the processing of mammograms. The mammograms are mainly analysed by radiologists who look for abnormalities that might indicate breast cancer. These abnormalities include small calcifications, masses and focal asymmetries.
Various breast imaging techniques now exist that attempt to detect breast cancer earlier and in a more predictable fashion.
Digital mammography, also called full-field digital mammography (FFDM), is a mammography system in which the x-ray film is replaced by solid-state detectors that convert x-rays into electrical signals. These detectors are similar to those found in digital cameras. The electrical signals are used to produce images of the breast that can be seen on a computer screen or printed on special film similar to conventional mammograms. From the patient's point of view, digital mammography is essentially the same as the screen-film system.
Computer-aided detection (CAD) systems use a digitised mammographic image that can be obtained from either a conventional film mammogram or a digitally acquired mammogram. The computer software then searches for abnormal areas of density or calcification that may indicate the presence of cancer. The CAD system highlights these areas on the images, alerting the radiologist to the need for further analysis.
The present invention uses change in breast tissue to identify the possible risk of cancer. The methods of the invention described below do not seek to locate features within the image used, but rather assign an overall score to the image which is indicative of the probability of the image being associated with a higher breast density and hence providing a measure of the risk of cancer.
Several approaches to automatic or semi-automatic assessment of mammographic breast density have been suggested previously. The majority of these have been aimed at reproducing the radiologists' categorical rating system. Boone et al [Journal of Digital Imaging 11(3) August 1998, 101-115] aimed at making a continuously scaled breast density index. Six mathematical features were calculated from the mammograms and used in conjunction with single value decomposition and multiple linear regression to calculate a computerised breast density. The training was done using a collection of mammograms sorted by their density as perceived by an expert.
Karssemeijer [Physics in Medicine and Biology 43 (1998) 365-378] divided the breast area into different regions and extracted features based on the grey level histograms of these regions. Using these features a kNN classifier is trained to classify a mammogram into one of four density categories. Byng et al [Physics in Medicine and Biology 41 (1996) 909-923] used measures of the skewness of the grey level histogram and of image texture characterised by the fractal dimension. They showed that both measures are correlated with the radiologists' classifications of the mammographic density. Tromans et al and Petroudi et al [in Astley et al; International workshop on Digital Mammography, Springer 2006, 26-33 and 609-615] used automated density assessment employing both physics based modelling and texture based learning of BI-RADS categories and Wolfe Patterns.
The Breast Imaging Reporting and Data System (BI-RADS) is a four category scheme proposed by the American College of Radiology. The BI-RADS categories are:    1. Entirely fatty    2. Fatty with scattered fibroglandular tissue    3. Heterogeneously dense    4. Extremely dense.
In practice, these classifications are used to alert clinicians that the ability to detect small cancers in the dense breast is reduced. The four categories are represented by the numbers one to four in order of increasing density.
Others, including Zhou et al [Medical physics 28(6), June 2001, 1056-1069] have used thresholding of the image based on properties of the grey level histogram to get an estimate of the percentage of density in the breast or (Yaffe et al [Physics in Medicine and Biology 39 (1994) 162938]) use thresholding done by a radiologist. In the thresholding method, the reading radiologist determines an intensity threshold using a slider in a graphical user interface. The radiologist is assisted visually by a display showing the amount of dense tissue corresponding to the current slider position. The density is defined as the ratio between segmented dense tissue and total area of breast tissue. The continuous nature of such threshold adjustment makes the method more sensitive than the BI-RADS, Wolfe patterns and related scoring systems with a low number of categories when detecting or monitoring, perhaps small, density changes.
A currently frequently discussed issue related to breast cancer risk is the potential influence of hormone replacement therapy taken after the menopause. If breast density is indeed a surrogate measure of risk for developing cancer in the breast, a sensitive measure of changes in breast density during hormone dosing provides an estimate of the gynecological safety of a given treatment modality. Hence, the concept of breast density has an ongoing interest.
The meaning of the word density depends on the context. The physical density states how much the breast tissue attenuates x-rays locally. An assessment of the projected area and specifically the distribution of fibroglandular tissue is often called dense tissue, and can be thought of as a “biological density”. This can be considered as an intrinsic property of the entire breast, and is the type of density referred to in the context of Wolfe Patters and related assessments.